Information processing device, sensor device, information processing system, and storage medium

ABSTRACT

An information processing system includes processing circuitry that is configured to receive input data from a shock sensor which outputs data based on a shock on the shock sensor, and identify a target segment of time-series data that is output from a motion sensor that senses a motion of an object. The target segment includes a pre-shock portion that occurs before the shock event and a post-shock portion that occurs after the shock event, the shock event is recognized based on the data from the shock sensor.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Japanese Priority PatentApplication JP 2013-060062 filed Mar. 22, 2013, the entire contents ofwhich are incorporated herein by reference.

BACKGROUND

The present technology relates to an information processing device, asensor device, an information processing system, and a storage medium.

Up to the present, many technologies for assisting users in becomingproficient at sports using sensing or analysis have been developed. Inthe technologies, statistical analysis of plays of users themselves orother users is used as one method. In the statistical analysis of plays,determination of motion patterns in plays can be useful in, for example,ball sports. Motion patterns are obtained by patterning specific motionsshown in plays of sports. For example, in the case of tennis, motionpatterns can be set for plays such as a serve, a smash, a forehandstroke, and a backhand stroke. By determining the motion patternsobserved in plays, for example, how a user makes a play can bequantitatively expressed with ease.

The determination of motion patterns in such sports has heretofore beenperformed by supporters of users' plays such as coaches, scorers, ormanagers. The supporters visually confirm users' plays and recordspecific motions upon observation. For such man-powered motion analysis,much effort is necessary. Further, it is difficult for users playingsports to perform the motion analysis on their own.

Accordingly, technologies for automatically analyzing motions byattaching sensor devices on which acceleration sensors, gyro sensors, orthe like are mounted on users or equipment used by the users andanalyzing data output from the sensors have been suggested. For example,Japanese Unexamined Patent Application Publication No. 2012-157644,Japanese Unexamined Patent Application Publication No. 2012-130415, andJapanese Unexamined Patent Application Publication No. 2012-120579disclose technologies for extracting feature information of swings basedon data output from motion sensors.

SUMMARY

For example, in the technology disclosed in Japanese Unexamined PatentApplication Publication No. 2012-157644, a process of searching fortimings of segments of a swing appearing in data output from a motionsensor is performed before feature information of the swing isextracted. In this case, however, a processing load may be increasedsince the process of searching for segments corresponding to featuremotions in the data output from the motion sensor is repeated. Further,accuracy of the determination of a motion pattern is not high eithersince setting of the segments is not stable. Japanese Unexamined PatentApplication Publication No. 2012-130415 and Japanese Unexamined PatentApplication Publication No. 2012-120579 do not suggest improvementcountermeasures for this problem either.

It is desirable to provide a novel and improved information processingdevice, a novel and improved sensor device, a novel and improvedinformation processing system, and a novel and improved storage mediumcapable of improving accuracy of determination by suitably settingsegments to be analyzed when detection values of a sensor detecting aphysical behavior of a sensor device are analyzed and a motion patternof a user is determined.

According to one embodiment, an information processing system isdescribed the includes processing circuitry configured to

receive input data from a shock sensor which outputs data based on ashock on the shock sensor, and

identify a target segment of time-series data that is output from amotion sensor that senses a motion of an object, wherein

the target segment includes a pre-shock portion that occurs before ashock event and a post-shock portion that occurs after the shock event,the shock event is recognized based on the data from the shock sensor.

According to a method embodiment, the method includes

receiving input data from a shock sensor which is configured to outputdata based on a shock on the shock sensor;

receiving time-series data from a motion sensor that senses motion of anobject; and

identifying with processing circuitry a target segment of thetime-series data, wherein

the target segment includes a pre-shock portion that occurs before ashock event and a post-shock portion that occurs after the shock event,the shock event is recognized based on the data from the shock sensor.

According to a non-transitory computer readable storage deviceembodiment, the device includes instructions that when executed by aprocessor configure the processor to implement an information processingmethod, the method comprising:

receiving input data from a shock sensor which is configured to detect ashock event;

receiving time-series data from a motion sensor that senses motion of anobject; and

identifying with processing circuitry a target segment of thetime-series data, wherein

the target segment includes a pre-shock portion that occurs before theshock and a post-shock portion that occurs after the shock.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the overview of an informationprocessing system according to an embodiment of the present technology;

FIG. 2 is a diagram illustrating the overview of a motion patterndetermination process according to an embodiment of the presenttechnology;

FIG. 3 is a block diagram illustrating a schematic functionalconfiguration of an information processing system according to anembodiment of the present technology;

FIG. 4 is a diagram for describing a shock sensor and a motion sensoraccording to an embodiment of the present technology;

FIG. 5 is a flowchart illustrating an example of a process according toan embodiment of the present technology;

FIG. 6 is a flowchart illustrating a reference example of an example ofFIG. 5;

FIG. 7 is a diagram for describing setting of a motion segment in theexample of FIG. 5;

FIG. 8 is a diagram for describing setting of a motion segment of theexample of FIG. 6;

FIG. 9 is a graph illustrating a specific example of sensor dataaccording to an embodiment of the present technology;

FIGS. 10A and 10B are graphs illustrating specific examples of sensordata according to an embodiment of the present technology;

FIG. 11 is a diagram illustrating a first screen example according to anembodiment of the present technology;

FIG. 12 is a diagram illustrating other examples of motion patterndisplays according to an embodiment of the present technology;

FIG. 13 is a diagram illustrating examples in which impact positiondisplays are changed according to motion patterns according to anembodiment of the present technology;

FIG. 14 is a diagram illustrating display examples when the impactposition displays are reversed according to an embodiment of the presenttechnology;

FIG. 15 is a diagram illustrating examples of impact positiondistribution displays according to an embodiment of the presenttechnology;

FIG. 16 is a diagram illustrating a second screen example according toan embodiment of the present technology;

FIG. 17 is a diagram illustrating a third screen example according to anembodiment of the present technology;

FIG. 18 is a diagram illustrating a fourth screen example according toan embodiment of the present technology;

FIG. 19 is a diagram illustrating an example of a hardware configurationof a sensor device according to an embodiment of the present technology;and

FIG. 20 is a diagram illustrating an example of a hardware configurationof an analysis device according to an embodiment of the presenttechnology.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, preferred embodiments of the present disclosure will bedescribed in detail with reference to the appended drawings. Note that,in this specification and the appended drawings, structural elementsthat have substantially the same function and structure are denoted withthe same reference numerals, and repeated explanation of thesestructural elements is omitted.

The description will be made in the following order.

1. Overview

2. Functional configuration

3. Processing flow

4. Specific example

5. Example of output information

6. Hardware configuration

7. Supplement

(1. Overview)

First, the overview of an embodiment of the present technology will bedescribed with reference to FIGS. 1 and 2. The description of theoverview includes description of the overview of an informationprocessing system according to the embodiment and description of theoverview of a motion identification process performed by the informationprocessing system.

(Overview of Information Processing System)

FIG. 1 is a diagram illustrating the overview of the informationprocessing system according to the embodiment of the present technology.Referring to FIG. 1, an information processing system 10 includes asensor device 100 and an analysis device 200.

The sensor device 100 is mounted directly or indirectly on a user whoplays sports. When the sensor device 100 is mounted directly on theuser, for example, as illustrated in the drawing, the sensor device 100may be configured to have a wristlet shape and may be mounted directlyon the body of the user. When the sensor device 100 is mountedindirectly on the user, the sensor device 100 may be wound around, sewnon, or attached to sports equipment (for example, a racket, clothing,shoes, a wristband, or the like in a case of tennis) which the userholds or wears or may be included in equipment in advance.

Here, the sensor device 100 acquires sensor information indicating aphysical behavior (a position, a speed, an acceleration, or the like) ofthe sensor device 100 itself. A physical behavior of the user orequipment is reflected in the sensor information. In this embodiment,the sensor device 100 includes at least two sensors to acquire suchsensor information. A first sensor of the sensor device 100 detects ashock transferred from the user or the equipment. The first sensor mayinclude, for example, a uniaxial acceleration sensor used as a shocksensor. On the other hand, a second sensor detects a behavior of thesensor device 100 with a higher resolution than that of the firstsensor. The second sensor may include, for example, a triaxialacceleration sensor, a gyro sensor, and a geomagnetic sensor used asmotion sensors. The sensor device 100 may further include one sensor ora plurality of sensors that detect an acceleration, an angular speed,vibration, a temperature, a time, or a position (for example, a positionon the surface of ground expressed by a latitude and a longitude or arelative position to a court or the like) in addition to the first andsecond sensors. The sensor device 100 transmits time-series dataobtained from such sensors to the analysis device 200.

The analysis device 200 analyzes the time-series data transmitted fromthe sensor device 100 to determine at least a motion pattern of a user.The analysis device 200 is illustrated as a server on a network.However, for example, any device may be used as long as the analysisdevice 200 is an information processing device that has a function ofanalyzing data through calculation using a processor a centralprocessing unit (CPU) or the like. As another example, the analysisdevice 200 may be, for example, a terminal device such as a smartphone,a tablet terminal, or various personal computers (PCs). When theanalysis device 200 is realized as a terminal device, the analysisdevice 200 may output information indicating the determined motionpattern of the user. Alternatively, when the analysis device 200 isrealized as a server, the analysis device 200 may transmit theinformation indicating the determined motion pattern of the user to, forexample, a client 300 such as a terminal device used in a house by theuser. Based on the determination result of the motion pattern, theanalysis device 200 may output statistical information indicating, forexample, how many times the user performs a motion or may outputinformation indicating a tendency (for example, a position of a ball hitby equipment, power or rotation given to a ball, or the like in a caseof ball sports) of the user in each motion.

When the analysis device 200 is realized as a server as in the exampleillustrated in the drawing, the function of the server may be realizedby a single server device or may be distributed and realized by aplurality of server devices connected via a network. Further, thefunction of the analysis device 200 may be distributed and realized in aserver and a terminal device connected via a network.

(Overview of Motion Pattern Determination Process)

FIG. 2 is a diagram illustrating the overview of a motion patterndetermination process according to the embodiment of the presenttechnology. In the motion pattern determination process, as illustratedin FIG. 2, time-series data 1001 and time-series data 1003 output fromthe two sensors included in the sensor device 100 are used as inputs.

In the example illustrated in the drawing, the time-series data 1001includes data output from the first acceleration sensor (uniaxial). Thefirst acceleration sensor is a so-called shock sensor. The firstacceleration can detect an acceleration having a larger dynamic rangeand a relatively high frequency occurring due to a shock, whereas theresolution of the acceleration is not particularly high. On the otherhand, the time-series data 1003 includes data output from the secondacceleration sensor (triaxial). The second acceleration sensor is aso-called motion sensor. The second acceleration sensor can detect anacceleration with a relatively low frequency containing a stationarycomponent like the force of gravity and has a high resolution of theacceleration, whereas the dynamic range is relatively narrow (there is aprobability of an acceleration that occurs due to a shock exceeding adetectable range).

In identification calculation for determining a motion pattern, thetime-series data 1003 with high resolution is basically used. Further,in this embodiment, a motion segment setting process (impact detectionprocess) is performed as preprocessing using the time-series data 1001.In the motion segment setting process, an analysis target segment in thetime-series data 1003 is set based on the time-series data 1001. Thatis, through the preprocessing, an identification calculation target inthe time-series data 1003 is restricted to the analysis target segmentset based on the time-series data 1001.

In many sports, a feature motion which is a motion pattern determinationtarget may be, for example, hitting of a ball by equipment or the bodyof a user, stomping of a user on the surface of the ground, or collidingwith another user. Since such a motion causes a shock applied to a useror equipment and vibration occurring due to the impact, portions oneither side of a point at which vibration is detected in the time-seriesdata 1001 of the shock sensor can be specified as segments (motionsegments) corresponding to a motion.

After the motion segment setting process, the features of thetime-series data 1003 are extracted from the set analysis targetsegment. Here, for example, frequency features of a signal waveform orstatistical features of a time waveform such as an average, adispersion, a minimum value, and a maximum value are extracted byperforming signal processing on the time-series data 1003. For example,FIGS. 10A and 10B illustrate sensor output waveforms at the time of aforehand stroke and a serve. In comparison of the waveforms,particularly, features are different in amplitudes of time waveformssuch as ω-X and ω-Z. Before the extraction of the features, missingportions of signal waveforms may be interpolated. The extracted featuresmay be subjected to a statistical process such as normalization.

Subsequently, the extracted features are subjected to identificationcalculation. The identification calculation is a calculation process ofidentifying a motion by specifying a motion pattern corresponding to theextracted features with reference to an identification dictionary(sports model) prepared beforehand. For example, the identificationdictionary may be generated for each sport and classifications may beswitched by setting of a user. As the identification calculation, forexample, a non-rule based method of forming identification parametersfrom data by machine learning such as a neural network, a Support VectorMachine (SVM), a k-neighborhood identifier, and Bayes' classificationcan be used.

As in the time-series data 1003, features may be extracted from thetime-series data 1001 and the extracted features may be used for theidentification calculation. That is, the time-series data 1001 of theshock sensor may be used not only in the setting of the motion segmentbut also in the identification calculation along with the time-seriesdata 1003 of the motion sensor.

When the determination of the motion pattern succeeds as a result of theidentification calculation, motion pattern information 2001 is output.The motion pattern information 2001 is, for example, information fornotifying a user of a motion pattern (in the example illustrated in thedrawing, a shot type of tennis=forehand stroke is shown), as illustratedin the drawing, and may further include the above-described statisticalinformation or information indicating a tendency of the user.

Examples of definition of motion patterns are shown in the followingTable 1. Thus, motion patterns may be defined for each sport or may bedefined for a category of sports. The foregoing identificationdictionary may be generated for each sport or category of sports and thesport or the category of sports may be switched by setting of a user.The motion patterns shown here are merely examples, and thus varioustypes of motion patterns can be defined in various other sports.

TABLE 1 ITEM TYPE PATTERN DETAILS TENNIS SWINGS FOREHAND STROKE FOREHANDVOLLEY FOREHAND SLICE BACKHAND STROKE BACKHAND VOLLEY BACKHAND SLICESMASH SERVE BASEBALL SWINGS UPPER SWING LEVEL SWING DOWN SWING BUNTSOCCER SHOTS SHOT LOB SHOT DRIVE SHOT VOLLEY SHOT OVERHEAD SHOT TABLETENNIS SWINGS FOREHAND FOREHAND CUT FOREHAND DRIVE BACKHAND BACKHAND CUTBACKHAND DRIVE SERVE(2. Functional Configuration)

Next, functional configurations of devices included in the informationprocessing system according to the embodiment of the present technologywill be described with reference to FIGS. 3 and 4.

FIG. 3 is a block diagram illustrating a schematic functionalconfiguration of the information processing system according to theembodiment of the present technology. Hereinafter, the functionalconfigurations of each device will be described with reference to FIG.3. A hardware configuration for realizing the functions will bedescribed later.

(Sensor Device)

The sensor device 100 includes a sensor 101, a preprocessing unit 107,and a communication unit 109.

The sensor 101 includes a shock sensor 103 and a motion sensor 105. Theshock sensor 103 is the first sensor that detects a shock transferredfrom a user or equipment in the sensor device 100 and may include auniaxial acceleration sensor according to this embodiment. The motionsensor 105 is the second sensor that detects a behavior of the sensordevice 100 at a resolution higher than that of the first sensor and mayinclude a triaxial acceleration sensor, a gyro sensor, or a geomagneticsensor in this embodiment. The sensor 101 may further include anothersensor such as a temperature sensor, a clock, or a Global PositioningSystem (GPS) receiver in addition to the shock sensor 103 and the motionsensor 105.

Here, differences between the shock sensor 103 and the motion sensor 105included in the sensor 101 will be described further with reference toFIG. 4. FIG. 4 is a diagram illustrating the shock sensor and the motionsensor according to the embodiment of the present technology. In thefollowing description, a case in which both of the shock sensor 103 andthe motion sensor 105 are acceleration sensors will be described, butthe same applies to a case in which other types of sensors are used.

Referring to FIG. 4, the shock sensor 103 has a larger dynamic range ofacceleration values than the motion sensor 105. For example, the shocksensor 103 can detect a large acceleration instantaneously occurring dueto a shock applied to a user or equipment. However, the resolution ofthe acceleration of the shock sensor 103 is lower than that of themotion sensor 105. Further, the shock sensor 103 does not detect anacceleration change of a low frequency. For example, an accelerationchange occurring when a user swings a part of the body or equipment onwhich the sensor device 100 is mounted is not detected by the shocksensor 103, since a frequency is low. Further, since the accelerationcaused due to the force of gravity of the earth is a stationarycomponent which does not vibrate, this acceleration is not detected bythe shock sensor 103. On the other hand, an acceleration change of ahigh frequency can be detected by the shock sensor 103, which can detecta higher frequency than the motion sensor 105. For example, a frequencycomponent of an eigenfrequency of a user or equipment can be detected.

On the other hand, the motion sensor 105 has a narrower dynamic range ofacceleration values than the shock sensor 103. Accordingly, for example,there is a probability of a large acceleration that occursinstantaneously due to a shock applied to a user or equipment exceedinga detectable range of the motion sensor 105. Further, with regard to afrequency of an acceleration change, the motion sensor 105 can merelydetect a frequency lower than that of the shock sensor 103. However, theresolution of the acceleration of the motion sensor 105 is higher thanthat of the shock sensor 103 and acceleration data can be output to thedegree of accuracy sufficient for motion pattern determination. Further,the motion sensor 105 can detect an acceleration change of a lowfrequency undetected by the shock sensor 103. For example, the motionsensor 105 can detect an acceleration change occurring when a userswings a part of the body or equipment on which the sensor device 100 ismounted. Further, the motion sensor 105 can also detect the accelerationcaused due to the force of gravity of the earth. Accordingly, forexample, by using a triaxial acceleration sensor as the motion sensor105, it is possible to specify a direction of the acceleration occurringby setting the direction of the force of gravity as a reference.

The shock sensor 103 and the motion sensor 105 can be realized, forexample, by changing the sensitivity or the number of axes ofacceleration sensors operating on the same principle. These sensors canbe realized, for example, using piezoresistance type or electrostaticcapacitance type acceleration sensors. The sensitivity of theacceleration sensor used as the shock sensor 103 can be set to be lowerthan the sensitivity of the acceleration sensor used as the motionsensor 105. Of course, the shock sensor 103 and the motion sensor 105may be realized by acceleration sensors that operate on differentprinciples. As described above, the motion sensor 105 can furtherinclude a gyro sensor or a geomagnetic sensor.

Referring back to FIG. 3, the preprocessing unit 107 performspreprocessing on data detected by the sensor 101. The preprocessing caninclude, for example, amplification of the detected data or filtering ofdata equal to or less than a threshold value. Through such processing,the preprocessing unit 107 generate analysis target data to betransmitted to the analysis device 200 based on first time-series dataincluding a detected value of the shock sensor 103 and secondtime-series data including a detected value of the motion sensor 105.

In this embodiment, one of the preprocessing unit 107 of the sensordevice 100 and a preprocessing unit 203 of the analysis device 200functions as a segment setting unit. The segment setting unit sets ananalysis target segment in the second time-series data including adetected value of the motion sensor 105 based on the first time-seriesdata including a detected value of the shock sensor 103. In thisprocess, the above-described motion segment setting is performed (ananalysis target segment corresponds to a motion segment).

Here, when the preprocessing unit 107 functions as the segment settingunit, the preprocessing unit 107 may provide only the second time-seriesdata in the set analysis target segment as the analysis target data tothe communication unit 109. When a transmission target by thecommunication unit 109 is restricted to data of the analysis targetsegment, an effect of reducing power consumption by reduction in anamount of communication can be expected. Alternatively, thepreprocessing unit 107 may generate separate data for indicating thedetected analysis target segment using a time stamp or the like of thetime-series data and provide the separate data as analysis target datatogether with the second time-series data to the communication unit 109.

However, when the preprocessing unit 203 of the analysis device 200functions as the segment setting unit rather than the preprocessing unit107, the preprocessing unit 107 associates the first time-series dataand the second time-series data subjected to the above-describedpreprocessing with each other using, for example, a time stamp andprovides the associated data as the analysis target data to thecommunication unit 109.

The communication unit 109 transmits the analysis target data providedfrom the preprocessing unit 107 to the analysis device 200. The analysistarget data may be transmitted using, for example, wirelesscommunication. The communication method is not particularly limited.However, for example, when the analysis device 200 is a server on anetwork, the Internet or the like can be used. When the analysis device200 is located near the sensor device 100, for example, Bluetooth® or awireless area network (LAN) may be used. Since the analysis target datamay not necessarily be transmitted in real time to the analysis device200, for example, the analysis target data may be transmitted to theanalysis device 200 through wired communication, for example, after aplay ends.

(Analysis Device)

The analysis device 200 includes a communication unit 201, thepreprocessing unit 203, and an analysis unit 205. The analysis device200 may further include a storage unit 209 and an output unit 211. Ahardware configuration realizing such functions will be described later.

The communication unit 201 receives the analysis target data transmittedfrom the sensor device 100. As described regarding the sensor device100, the analysis target data can be transmitted using, for example,network communication such as the Internet, wireless communication suchas Bluetooth® or wireless LAN, or wired communication. The communicationunit 201 provides the received sensor information to the preprocessingunit 203. As will be described later, when the preprocessing unit 203 isnot installed, the sensor information may be provided directly to theanalysis unit 205.

The preprocessing unit 203 performs preprocessing on the data receivedby the communication unit 201. For example, the preprocessing unit 203can function as the above-described segment setting unit. When thepreprocessing unit 203 functions as the segment setting unit, thepreprocessing unit 203 sets an analysis target segment (motion segment)in the same second time-series data (data obtained by the motion sensor105 of the sensor device 100) received by the communication unit 201based on the first time-series data (data obtained from the shock sensor103 of the sensor device 100) received by the communication unit 201.The preprocessing unit 203 may provide only the second time-series datain the set analysis target segment to the analysis unit 205.Alternatively, the preprocessing unit 203 may generate separate data forindicating the set analysis target segment using a time stamp or thelike of the time-series data and provide the separate data together withthe second time-series data to the analysis unit 205.

When the preprocessing unit 107 of the sensor device 100 functions asthe segment setting unit, the preprocessing in the analysis device 200is not necessary, and thus the preprocessing unit 203 is not installedin some cases. Alternatively, the preprocessing unit 203 not onlyfunctions as the segment setting unit, but may also perform a processsuch as amplification of data or filtering of data equal to or less thana threshold value in place of the preprocessing unit 107 of the sensordevice 100.

The analysis unit 205 performs analysis based on the second time-seriesdata (data obtained from the motion sensor 105 of the sensor device 100)in the analysis target segment set by the segment setting unit (thepreprocessing unit 107 or 203) and includes a motion patterndetermination unit 207 that determines a motion pattern of a user. Themotion pattern determination unit 207 may determine a motion patternusing the first time-series data (data obtained from the shock sensor103 of the sensor device 100) in addition to the second time-seriesdata. The analysis unit 205 may further have a function of analyzing aplay of a user based on data provided from the sensor device 100 inaddition to the motion pattern determination unit 207. The analysis unit205 may store the analysis result in the storage unit 209.

The output unit 211 is installed as necessary. In the exampleillustrated in FIG. 1, the analysis device 200 is realized as a server.Therefore, for example, the analysis result of the analysis unit 205including information regarding a motion pattern of a user istransmitted from the communication unit 201 to a terminal device and isoutput from the terminal device. On the other hand, when at least someof the functions of the analysis device 200 are realized by a terminaldevice such as a smartphone, a tablet terminal, or various PCs, theoutput unit 211 that outputs the analysis result is installed in theanalysis device 200 to output the analysis result as an image or audio.When the analysis result is displayed as an image, the image may includean image or text indicating a motion pattern of a user. An example ofinformation output from the output unit 211 will be described later.

(3. Processing Flow)

Next, an example of a process according to the embodiment of the presenttechnology will be described in comparison with a reference example withreference to FIGS. 5 to 8.

FIG. 5 is a flowchart illustrating an example of the process accordingto the embodiment of the present technology. Referring to FIG. 5, thepreprocessing unit 203 (or the preprocessing unit 107 of the sensordevice 100; can be interpreted as the preprocessing unit 107 althoughonly the preprocessing unit 203 is described representatively below insome cases) of the analysis device 200 acquires a detected value of theshock sensor 103 from the first time-series data (step S101).

Next, the preprocessing unit 203 determines whether the detected valueacquired in step S101 exceeds a predetermined threshold value (stepS103). Here, when the detected value exceeds the threshold value (YES),the preprocessing unit 203 estimates that this time point is an impactpoint (step S105) and allows the process to proceed to a motionidentification process to be described below. Conversely, when thedetected value does not exceed the threshold value in step S103 (NO),the preprocessing unit 203 acquires a detected value of the shock sensor103 in a subsequent time window without performing the motionidentification process any longer (step S101).

When the impact point is estimated in step S105, the preprocessing unit203 sets a motion segment centering on the impact point. For example,the length of the motion segment before and after the impact point canbe set using the longest motion segment as a criterion in eachidentifiable motion pattern. For example, in the example of tennis, whena motion pattern in which a feature motion is shown earliest when viewedfrom the impact point is a backhand volley, the motion segment is setusing the starting point (for example, the impact point 0.5 secondsprior) of the feature motion of the backhand volley as a starting point.Likewise, when a motion pattern in which a feature motion continueslater when viewed from the impact point is a forehand slice, the motionsegment is set using the ending point (for example, the impact point 1second after) of the feature motion of the forehand slice as an endingpoint. That is, the length of the motion segment is set such that afeature motion of each motion pattern is included.

Next, the analysis unit 205 extracts feature amounts from the set motionsegment (step S109). When the processes of step S101 to step S107 areperformed by the preprocessing unit 107 of the sensor device 100, atleast data of the motion segment is transmitted from the sensor device100 to the analysis device 200 between step S107 and step S109. Thefeatures extracted in step S109 can be frequency features of a signalwaveform or statistical features of a time waveform such as an average,a dispersion, a minimum value, and a maximum value, as described above.For example, sensor output waveforms at the times of a forehand strokeand a serve are illustrated in FIGS. 10A and 10B. When the sensor outputwaveforms are compared, different features are particularly shown in anamplitude of a time waveform such as ω-X or ω-Z. Before the extractionof the features, missing portions of signal waveforms may beinterpolated. The extracted features may be subjected to a statisticalprocess such as normalization.

Next, the analysis unit 205 performs identification calculation based onthe feature amounts extracted in step S109 (step S111). As describedabove, referring to the identification dictionary prepared beforehand,the identification calculation can be performed, for example, using anon-rule based method of forming identification parameters from data bymachine learning such as a neural network, an SVM, a k-neighborhoodidentifier, and Bayes' classification. Thereafter, the analysis unit 205outputs a result of the identification calculation (step S113). Theresult of the identification calculation can be stored in the storageunit 209. Thereafter, the analysis result may be transmitted to aterminal device which the user uses via the communication unit or may beoutput from the self-output unit 211 of the analysis device 200.

In a modification example of this embodiment, the estimation of theimpact point in step S103 and step S105 described above may besubstituted with another step. For example, when time-series data ofdetected values of the shock sensor is subjected to a Fourier transformand a frequency feature including an eigenfrequency of the user orequipment on which the sensor device 100 is mounted is detected, animpact point may be estimated to be present in the segment and a motionsegment may be set centering on the segment.

(Reference Example)

FIG. 6 is a flowchart illustrating a reference example of the example ofFIG. 5. In the example illustrated in FIG. 6, the detected value of theshock sensor 103 is not used for a process (the sensor device 100 doesnot include the shock sensor 103) and the setting of the motion segmentin the preprocessing unit 203 (or the preprocessing unit 107) is notperformed.

In the example illustrated in the drawing, the analysis unit acquires adetected value of the motion sensor (step S201). In the referenceexample, the detected value of the motion sensor is provided as uniquetime-series data. Next, the analysis unit sets a motion segment (stepS203). Here, since no detected value of the shock sensor is providedunlike the example of FIG. 5, the analysis unit sets the motion segmentwithout a clue. For example, the analysis unit sets motion segments inorder from a starting point of time-series data obtained from the motionsensor. For example, the length of the motion segment can be set as inthe example of FIG. 5.

Next, as in the example of FIG. 5, the analysis unit extracts thefeature amounts from the motion segment (step 109) and further performsthe identification calculation (step S111). However, since the motionsegment in step S203 is set without a clue, the motion pattern may notbe identified in identification calculation of step S111 in some cases.On the other hand, the motion segment does not correspond to an actualfeature motion and the motion pattern is not identifiable in theidentification calculation in many cases.

Thus, the analysis unit determines whether a motion is identified in theidentification calculation (step S205). When the motion is identified(YES), the analysis unit outputs a result of the identification resultas in the example of FIG. 5 (step S113). Conversely, when the motion isnot identified in step S205 (NO), the analysis unit resets a motionsegment (step S207). The motion segment is reset, for example, byoffsetting the segment set in step S203 (or step S207 of the previoustime) by a predetermined time. For the reset motion segment, theanalysis process of step S109 and step S111 is performed again.

FIGS. 7 and 8 are diagrams for describing the setting of the motionsegment in the examples of FIGS. 5 and 6 described above. Hereinafter,the advantages of the process according to this embodiment in comparisonof the reference example will be described with reference to thesedrawings.

In this embodiment, as illustrated in FIG. 7, a motion segment 1005 intime-series data 1003 of the motion sensor is set based on time-seriesdata 1001 of the shock sensor. Accordingly, for example, a segment inwhich a feature motion such as hitting of a ball by equipment or thebody of a user, stomping of a user on the surface of the ground, or acolliding action with another user is highly likely to occur can be setin the motion segment 1005 from the beginning.

On the other hand, in the reference example illustrated in FIG. 8, thereis no clue for setting the motion segment 1005 in the time-series data1003 of the motion sensor. Therefore, for example, the motion segmentset by guess, as mentioned in step S203 of FIG. 6, is repeatedly reset,as described in step S207, and thus a suitable motion segment is found.In the example illustrated in FIG. 8, motion segments 1005 a, 1005 b,and 1005 c are sequentially set. When a feature amount in one of themotion segments indicates a feature “motion-like” and the motion isidentified, the fact that the motion segment is the suitable motionsegment is determined afterwards.

In both of the foregoing examples of FIGS. 7 and 8, the determination ofthe motion pattern itself can be made. In the reference exampleillustrated in FIG. 8, however, from a statistical viewpoint, it isnecessary to repeat the identification calculation in the motion segmentset by guessing several times in order to suitably set one motionsegment. Accordingly, the calculation cost necessary for thedetermination of the motion pattern may be high. Further, when offsetwidths (offset widths of the segment 1005 a→the segment 1005 b and thesegment 1005 b→the segment 1005 c illustrated in the example of FIG. 8)at the time of the update of the motion segment are set to be large tosuppress the calculation cost, for example, there is a probability ofthe set motion segment temporally passing the suitable motion segment,thereby deteriorating accuracy of the determination of the motionpattern.

On the other hand, in the example of this embodiment illustrated in FIG.7, the suitable motion segment can be set from the beginning. Therefore,the calculation cost can be suppressed without deterioration in theaccuracy of the determination of the motion pattern. In the example ofthis embodiment, however, as described in FIG. 7, the motion segment isset based on the time-series data 1003 of the shock sensor. Therefore,it is difficult to apply the setting of the motion segment to a motionoccurring without a shock detected by the shock sensor, for example, anair swing. When it is necessary to identify such a motion, the processas in the reference example illustrated in FIG. 8 can be used.

As described above, a dynamic range of the motion sensor with respect toa value of an acceleration is relatively small. Therefore, when a shockis applied to a user or equipment, the occurring acceleration is notaccurately detected in many cases since the acceleration exceeds thedynamic range. Therefore, it is difficult to use the same sensor as bothof the shock sensor and the motion sensor. Accordingly, in thisembodiment, a shock is detected using a shock sensor which is a separatesensor and the detected shock is used to set a motion segment. When anacceleration occurring due to a shock is within the dynamic range of themotion sensor in consideration of the nature of a sport or performanceof a sensor, the same sensor may be used as both of the shock sensor andthe motion sensor.

(4. Specific Example)

Next, a specific example of sensor data according to the embodiment ofthe present technology will be described with reference to FIGS. 9, 10A,and 10B.

FIG. 9 is a graph illustrating a specific example of the sensor dataaccording to the embodiment of the present technology. In FIG. 9,acceleration data (SHOCK) of the shock sensor, acceleration data (a-X,a-Y, and a-Z) of a triaxial acceleration sensor which is the motionsensor, and angular speed data (ω-X, ω-Y, and ω-Z) of a triaxial gyrosensor which is the same motion sensor are shown in a time-seriesmanner.

In this embodiment, as described above, the motion segment 1005 is setin the time-series data of the motion sensor based on the time-seriesdata of the shock sensor. In this example, the shock sensor detects anacceleration of a high frequency occurring due to a shock applied to auser or equipment, whereas the shock sensor does not detect anacceleration of another low frequency or a stationary component.Accordingly, for example, when any change is shown in the time-seriesdata of the shock sensor, the changed point can be regard as a shockoccurring time point, that is, an impact point. In the exampleillustrated in the drawing, points before and after the point at whichthe change is shown in the time-series data of the shock sensor areautomatically set as the motion segment 1005.

On the other hand, in a segment other than the motion segment 1005, achange is also shown in the acceleration data or the angular speed dataof the motion sensor. For example, segments 1007 a, 1007 b, and 1007 cshown in the drawing are not the motion segment. However, a change in anamplitude is shown to the same degree as the motion segment of theacceleration or the angular speed. For example, in the process ofdetermining the motion pattern in the reference example in which thedata of the shock sensor is not used, as illustrated in FIGS. 6 and 8described above, these segments may be suspicious segments in which aspecific motion occurs or does not occur. That is, although a specificmotion actually occurs in the motion segment 1005, there is aprobability of erroneous recognition that a motion also occurs in thesegment 1007. In this embodiment, since the suspicious segments can beexcluded from identification calculation targets under the condition inwhich a specific motion is accompanied with a shock, the calculationcost can be suppressed and the accuracy of the determination of themotion pattern can be improved.

FIGS. 10A and 10B are graphs illustrating specific examples of thesensor data according to the embodiment of the present technology. InFIGS. 10A and 10B, acceleration data (SHOCK) of the shock sensor,acceleration data (a-X, a-Y, and a-Z) of the triaxial accelerationsensor which is the motion sensor, and angular speed data (ω-X, ω-Y, andω-Z) of the triaxial gyro sensor which is the same motion sensor areshown in a time-series manner, as in FIG. 9.

In FIGS. 10A and 10B, the case of a forehand stroke of tennis and thecase of a serve of tennis are illustrated respectively as examples ofthe sensor data corresponding to the motion pattern. In both of theexamples, feature waveforms are shown in the time-series data of themotion sensor, but the motion segment 1005 set based on the time-seriesdata of the shock sensor covers the feature waveforms. From theexamples, it can be understood that the setting of the motion segmentbased on the data of the shock sensor according to this embodiment iseffective for improvement of the accuracy of the determination of themotion pattern and suppression in the calculation cost.

(5. Example of Output Information)

Next, an example of information to be output according to the embodimentof the present technology will be described with reference to FIGS. 11to 18. The information to be output according to this embodiment caninclude, for example, information indicating the motion pattern which isthe analysis result of the analysis unit 205 of the analysis device 200,but various other kinds of information may be added. Since the addedinformation can be generated appropriately using, for example, atechnology of the related art, the information to be output will bemainly described in the following description and the detaileddescription of a method of generating the added information will beomitted.

In the following description, a screen displayed mainly in a displaywill be described, but the information to be output according to thisembodiment of the present technology is not limited to an image or textdisplayed on a screen. For example, the information may be output asaudio from a speaker, may be output as visual information other than animage by a lamp or the like, or may be output by vibration. As describedabove, for example, the information may out be output from an outputunit of the self-analysis device 200 when the analysis device 200 is aterminal device. The information may be output from an output unit of aterminal device of a client connected to a server on a network when theanalysis device 200 is the server on the network.

FIG. 11 is a diagram illustrating a first screen example according tothe embodiment of the present technology. Referring to FIG. 11, a screen2100 includes a motion pattern display 2101, a waveform display 2103, animpact position display 2105, and a SHOTVALUE display 2107.

The motion pattern display 2101 displays a motion pattern (which isexpressed as a SHOTTYPE in this screen) determined in the analysisdevice 200. In the example illustrated in the drawing, the fact that thedetermined motion pattern is a “forehand stroke” is displayed. Themotion pattern display 2101 may include, for example, an icon 2102 a andtext 2102 b showing the motion.

FIG. 12 is a diagram illustrating another example of the motion patterndisplay 2101. In FIG. 12, a motion pattern display 2101 a of “SMASH,” amotion pattern display 2101 b of “BACKHAND STROKE,” and a motion patterndisplay 2101 c of “FOREHAND STROKE” are illustrated. Of course, themotion pattern display 2101 is not limited to these examples. Forexample, as exemplified in Table 1 above, numerous other motion patternscan be defined for the item “tennis” and motion patterns can be definedlikewise for other items “baseball” and “soccer.” The motion patterndisplay 2101 can be set for each of the motion patterns defined in thisway.

Referring back to FIG. 11, for example, the waveform display 2103displays a waveform of time-series data detected by the sensor 101 ofthe sensor device 100. For example, the waveform display 2103 may bedisplayed as one of the visual effects. In the example illustrated inthe drawing, the waveform of the time-series data by the shock sensor isdisplayed as the waveform display 2103. However, the waveform of thetime-series data by the motion sensor may be likewise displayed or bothwaveforms thereof may be displayed.

The impact position display 2105 displays a position (impact position)which is specified by a process separate from the determination of themotion pattern and at which a ball hits a racket. Like the exampleillustrated in the drawing, when a motion pattern is an action (shot) ofhitting a ball, the user can comprehend the impact position due to thedisplay of the impact position display 2105. For example, it is possibleto obtain information indicating whether the impact position is anintended position or is deviated from an exemplary position. The objecthitting the ball need not be an instrument, but may also be part of auser's body, such as the user's arm or hand (e.g., playing handball).Other instruments for other sports, such as baseball bats, and golfclubs may be used as well.

FIG. 13 is a diagram illustrating an example in which the impactposition display 2105 is changed according to a motion pattern. In FIG.13, an impact position display 2105 a when the motion pattern is a“SMASH,” an impact position display 2105 b when the motion pattern is a“BACKHAND STROKE,” and an impact position display 2105 c when the motionpattern is a “FOREHAND STROKE” are illustrated. As in the example, adirection (a rotation angle in a planar direction) of a racket displayedat the impact position display 2105 may differ according to the motionpattern. By displaying the racket of the impact position display 2105 ina direction close to the actual direction of the racket in the case ofeach motion pattern, the user can comprehend the impact position of theball more intuitively. By operating a slider 2106, the user can freelychange the direction of the racket.

FIG. 14 is a diagram illustrating a display example when a check boxlabeled “Turn Over” included in the impact position display 2105 ischecked. In this case, the front and back of the racket are reversed anddisplayed at the impact position display 2105. In the exampleillustrated in the drawing, an impact position display 2105 d when themotion pattern is a “SMASH,” an impact position display 2105 e when themotion pattern is a “BACKHAND STROKE,” and an impact position display2105 f when the motion pattern is a “FOREHAND STROKE” are illustrated.In all of the displays, the front and back of the racket are reversed byrotating the racket 180° using a shaft as an axis, in comparison to eachimpact position display 2105 illustrated in FIG. 13.

Referring back to FIG. 11, the SHOTVALUE display 2107 displays variousindex values regarding the motion pattern (here, a shot) specifiedthrough a process separate from the determination of the motion pattern.In the example illustrated in the drawing, a sweet spot hit probability,a shot power, a spin type, and a swing speed are displayed, but otherindex values may be displayed.

FIG. 15 is a diagram illustrating an example of an impact positiondistribution display 2111 as another display example. The impactposition distribution display 2111 shows a statistical distribution ofthe impact positions specified together with the motion patterns (in theforegoing example, the smash, the backhand stroke, the forehand stroke,and the like) of the shot. A collection target can be changed with shottype selection 2112. For example, in an impact position distributiondisplay 2111 a, “ALL” is selected with the shot type selection 2112 anda distribution of the impact positions collected for all shot types isdisplayed. Further, for example, a distribution of the impact positionsmay be displayed according to color classification for each frequency,as illustrated in the drawing.

On the other hand, in the impact position distribution display 2111 b,the “FOREHAND STROKE” is selected with the shot type selection 2112 anda distribution of the impact positions collected when the shot type(which can be determined as the motion pattern) is the “FOREHAND STROKE”is displayed. Further, in the impact position distribution display 2111c, the “BACKHAND STROKE” is selected with the shot type selection 2112and a distribution of the impact positions collected when the shot typeis the “BACKHAND STROKE” is displayed. With the displays, the user canintuitively recognize the distribution of the impact positions for eachshot type. For example, when a tendency for the impact position todeviate from the intended position or an exemplary position is shown,the user can perform a play while being conscious of correction of theimpact position.

FIG. 16 is a diagram illustrating a second screen example according tothe embodiment of the present technology. Referring to FIG. 16, a screen2200 includes a shot type ratio display 2201. In this embodiment, sinceshot types (types of swings) can be determined through the determinationof the motion pattern, not only simple impact counters (the number ofshots or swings is counted) but also detailed statements of the shots orthe swings can be displayed by the shot type ratio display 2201. Byoperating a slider 2203, target periods of the shot type ratio display2201 can be selected from the past day, week, month, or year, from thevery beginning, and the like.

FIG. 17 is a diagram illustrating a third screen example according tothe embodiment of the present technology. Referring to FIG. 17, a screen2300 includes a score chart display 2301. In this embodiment, since shottypes (types of swings) can be determined through the determination ofthe motion pattern and information such as an impact position or thelike for each shot can be acquired, as illustrated in the example ofFIG. 11 or the like, a score can be calculated for each shot type andcan be displayed as the score chart display 2301. As in the example ofFIG. 16 described above, by operating a slider 2303, target periods ofthe score chart display 2301 can be selected from the past day, week,month, or year, from the very beginning, and the like.

In the example illustrated in the drawing, a score type selection 2305is also displayed. The score type selection 2305 is a display forselecting a score type displayed as the score chart display 2301 amongthree score types, i.e., a SweetSpot, SwingSpeed, and Mixed. Further, asweet spot score is a score indicating how close an impact position foreach shot is to a so-called sweet spot. A higher score indicates thatthe user hits a ball in a sweet spot more accurately at the time of theshot.

FIG. 18 is a diagram illustrating a fourth screen example according tothe embodiment of the present technology. Referring to FIG. 18, a screen2400 includes a swing speed graph display 2401. In this embodiment,since shot types (types of swings) can be determined through thedetermination of the motion pattern and information such as a swingspeed for each shot can be acquired, as illustrated in the example ofFIG. 11 or the like, the swing speed can be collected for each shot typeand can be displayed together with an average value or a maximum valueas the graph. As in the examples of FIGS. 16 and 17 described above, byoperating a slider 2403, target periods of the swing speed graph display2401 can be selected from the past day, week, month, or year, from thevery beginning, and the like.

(6. Hardware Configuration)

Next, examples of hardware configurations for realizing a sensor deviceand an analysis device according to embodiments of the presenttechnology will be described with reference to FIGS. 19 and 20.

(Sensor Device)

FIG. 19 is a diagram illustrating an example of a hardware configurationof a sensor device according to an embodiment of the present technology.For example, a sensor device 800 can be realized as the sensor device100 according to the foregoing embodiment.

The sensor device 800 includes a central processing unit (CPU) 801, aread-only memory (ROM) 802, a random access memory (RAM) 803, a sensor804, a user interface 805, an external storage device 806, acommunication device 807, and an output device 808. These constituentelements are connected to each other via, for example, a bus 809.

The CPU 801, the ROM 802, and the RAM 803 realize various functions in asoftware manner, for example, by reading and executing program commandsrecorded on the external storage device 806. In the embodiment of thepresent technology, for example, control of the entire sensor device 800or the functions of the preprocessing unit 107 descried in the foregoingexamples can be realized by the CPU 801, the ROM 802, and the RAM 803.

The sensor 804 corresponds to the sensor 101 in the functionalconfiguration of the foregoing embodiment. The sensor 804 can include,for example, an acceleration sensor, an angular speed sensor, avibration sensor, a temperature sensor, or a GPS receiver.

The user interface 805 receives a user's operation on the sensor device800 and can be, for example, an input device such as a button or a touchpanel. The user's operation is, for example, an operation instructingstart or end of transmission of sensor information from the sensordevice.

The external storage device 806 stores various kinds of informationregarding the sensor device 800. For example, the external storagedevice 806 may store program commands causing the CPU 801, the ROM 802,and the RAM 803 to realize the functions in a software manner or maytemporarily cache data acquired by the sensor 804. When the sensordevice 800 is considered to be mounted on the user himself or herself orsporting equipment, for example, a storage device such as asemiconductor memory that is strong against shock is used as theexternal storage device 806.

The communication device 807 corresponds to the communication unit 109in the functional configuration of the foregoing embodiment. Thecommunication device 807 communicates with an analysis device 900 to bedescribed below in conformity with various wired or wirelesscommunication schemes. The communication device 807 may communicatedirectly with the analysis device 900 through inter-device communicationor may communicate with the analysis device 900 via a network such asthe Internet.

The output device 808 is configured as a device that can visually oraudibly notify a user of the acquired information. The output device 808can be, for example, a display device such as a liquid crystal display(LCD) or an audio output device such as a speaker or a headphone.Although not described in the foregoing embodiment, when informationindicating an analysis result such as a motion pattern is fed back fromthe analysis device to the sensor device, for example, in anotherembodiment, the information can be output from the output device 808.The sensor device 800 may further include a lighting unit such as an LEDlamp or a vibrator that provides vibration to a user or equipment as anoutput unit.

(Analysis Device)

FIG. 20 is a diagram illustrating an example of a hardware configurationof an analysis device according to an embodiment of the presenttechnology. For example, an analysis device 900 can be realized as theanalysis device 200 according to the foregoing embodiment.

The analysis device 900 can include a CPU 901, a ROM 902, a RAM 903, auser interface 905, an external storage device 906, a communicationdevice 907, and an output device 908. These constituent elements areconnected to each other via, for example, a bus 909.

The CPU 901, the ROM 902, and the RAM 903 realize various functions in asoftware manner, for example, by reading and executing program commandsrecorded on the external storage device 906. In the embodiment of thepresent technology, for example, control of the entire analysis device900 or the functions of the preprocessing unit 203 and the analysis unit205 described in the foregoing examples can be realized by the CPU 901,the ROM 902, and the RAM 903.

The user interface 905 receives a user's operation on the analysisdevice 900 and can be, for example, an input device such as a button ora touch panel.

The external storage device 906 stores various kinds of informationregarding the analysis device 900. For example, the external storagedevice 906 may store program commands causing the CPU 901, the ROM 902,and the RAM 903 to realize the functions in a software manner or maytemporarily cache sensor information received by the communicationdevice 907. The external storage device 906 may function as the storageunit 209 described in the foregoing example and store logs such as thesensor information or the determination result of the motion pattern.

The output device 908 is configured as a device that can visually oraudibly notify a user of information. The output device 908 can be, forexample, a display device such as an LCD or an audio output device suchas a speaker or a headphone. When the analysis device 900 is a terminaldevice used by the user, the output device 908 causes the display deviceto display a result obtained through a process of the analysis device900 as text or an image or allows a speaker or the like to output theresult as audio.

The analysis device 900 may be implemented in a wearable computerdevice, such as a watch-type device, smartwatch, head mounted displaydevice, glass-type device, ring-type device for finger or smartglasses.Also, while much of the description has been directed to a tennisexample, the target segments of other motion patterns may be used aswell, such as a baseball swing (the shock event occurs when the baseballhits the baseball bat, and the user moves the baseball bat in certainmotions based on the type of pitch). Similarly, the shock sensor andmotion sensor may be attached to a shaft of a golf club and the shockevent occurs when the club strikes the golf ball.

The sensors communicate wirelessly with the wearable computer device sothat the type of shot, impact statistics, swing indices (2107 in FIG.11), etc. may be shown in the displays of the wearable computer devices.In this way, the user can view on his smartwatch, where the golf ballwas struck on the clubface, whether the swing was on plane, orinside/out, or outside/in, etc. Furthermore, the smartwatch could keeptrack of the number of strokes by counting the number of impact eventswith the golf ball. Each club could be equipped with sensors, or theuser's glove (or smartwatch) could be equipped with the sensors anddetect the impact event through conduction of the shock event throughthe user's skeletal structure.

(7. Supplement)

In the foregoing embodiment, the information processing system includingthe sensor device and the analysis device (both of which can beinformation processing devices) has been described. In an embodiment ofthe present technology, the information processing system furtherincludes for example, a server (includes a device realized as acollective of functions of a plurality of devices) on a network whichrealizes at least some of the functions of the analysis device, aprogram causing a computer to realize the functions of the device, and astorage medium which records the program and is a non-temporary tangiblemedium.

In the foregoing embodiment, the example in which one sensor device isused has been described. However, in an embodiment of the presenttechnology, a plurality of sensor devices may be used. For example, inthe case of tennis, a sensor device can be mounted on a racket which auser holds and other sensor devices may be mounted on shoes which theuser wears. By combining and analyzing data provided from these sensordevices, a motion pattern of a higher-level such as a dash, a jump, aswing during forward movement, or a swing during backward movement canbe determined. Even in this case, time-series data of a shock sensor ofeach sensor device can be used to specify a motion segment intime-series data of a motion sensor provided from each sensor device.When time-series data of motion sensors of sensor devices aresynchronized, motion segments in the time-series data of the motionsensors of all of the sensor devices may be specified using thetime-series data of one of the shock sensors of the devices.Alternatively, when each sensor device includes a shock sensor,time-series data of the motion sensor of each sensor device may besynchronized using the time-series data of the shock sensor.

In the foregoing embodiment, the example in which a motion pattern ofone user is determined has been described. However, in an embodiment ofthe present technology, the number of users who are motion patterndetermination targets may be plural. For example, the analysis devicemay receive sensor information from each of the sensor devices of aplurality of users and may determine a motion pattern of each user.Information including the motion patterns determined by the separateanalysis devices for the plurality of users may be shared via a networkand, for example, information obtained by comparing the users in theinformation shown in the example of the foregoing screen can beprovided.

In the foregoing embodiment, the example in which the sensor device andthe analysis device are separate devices has been described. In anembodiment of the present technology, the sensor device and the analysisdevice may be integrated. In this case, the sensor device can acquiretime-series data from a sensor, set a motion segment in the time-seriesdata, determine a motion pattern through analysis of the motion segment,and output a determination result itself or transmit the determinationto a server on a network or a terminal device.

The preferred embodiments of the present technology have been describedin detail with reference to the appended drawings, but the technicalrange of the present technology is not limited to the examples.

Additionally, the present technology may also be configured as below.

-   (1) An information processing system comprising: processing    circuitry configured to receive input data from a shock sensor which    is configured to output data based on a shock on the shock sensor,    and identify a target segment of time-series data that is output    from a motion sensor that senses a motion of an object, wherein the    target segment includes a pre-shock portion that occurs before a    shock event and a post-shock portion that occurs after the shock    event, the shock event is recognized based on the data from the    shock sensor.-   (2) The information processing system of (1), wherein the target    segment is identified based on the data from the shock sensor.-   (3) The information processing system of (1) or (2), wherein the    shock event comprises an impact event including an impact on an    object caused by a movement of a user.-   (4) The information processing system of any of (1) to (3), wherein    the object is an object held by the user, and the impact event    includes a collision by the object and another object.-   (5) The information processing system of any of (1) to (4), wherein    the another object is one of a tennis ball, a golf ball, and a    baseball.-   (6) The information processing system of any of (1) to (5), wherein    the object is a part of a body of the user.-   (7) The information processing system of any of (1) to (6), wherein    the shock sensor and the motion sensor are acceleration sensors.-   (8) The information processing system of any of (1) to (7), wherein    the system includes the shock sensor, the shock sensor having a    greater dynamic range in acceleration than the motion sensor.-   (9) The information processing system of any of (1) to (8), wherein    the system includes the motion sensor, the motion sensor having a    greater resolution of acceleration than the shock sensor.-   (10) The information processing system of any of (1) to (9), wherein    the motion sensor being configured to detect triaxial acceleration    and angular speed.-   (11) The information processing system of any of (1) to (10),    wherein the processing circuitry is configured to analyze the target    segment and identify a predetermined motion pattern of the object    that corresponds with the target segment.-   (12) The information processing system of any of (1) to (11),    wherein the processing circuitry includes an output device that    notifies a user of the predetermined motion pattern by generating at    least one of an audio signal and a video signal.-   (13) The information processing system of any of (1) to (12),    further comprising: a display configured to display an indication of    the predetermined motion pattern.-   (14) The information processing system of any of (1) to (13),    further comprising: a display configured to display an indication of    an impact position of another object on the object.-   (15) The information processing system of any of (1) to (14),    further comprising: a display configured to display at least one    index value associated with an actual motion pattern of the object.-   (16) The information processing system of any of (1) to (15),    further comprising: a display configured to display statistical    feedback to a user regarding a plurality of detected movement    patterns of the object.-   (17) The information processing system of any of (1) to (16),    wherein the motion sensor transmits the time-series data to the    processing circuitry wirelessly.-   (18) The information processing system of any of (1) to (17),    wherein the processing circuitry is configured to identify the    target segment by determining when the input data from the shock    sensor exceeds a predetermined threshold, and a frequency of a    signal described by the input data from the shock sensor.-   (19) The information processing system of any of (1) to (18),    wherein the information processing system is embodied in a    watch-type device, and the processing circuitry is configured by a    downloadable software application to identify the target segment.-   (20) An information processing method comprising: receiving input    data from a shock sensor which outputs data based on a shock on the    shock sensor; receiving time-series data from a motion sensor that    senses motion of an object; and identifying with processing    circuitry a target segment of the time-series data, wherein the    target segment includes a pre-shock portion that occurs before a    shock event and a post-shock portion that occurs after the shock    event, the shock event is recognized based on the data from the    shock sensor.-   (21) A non-transitory computer readable storage device including    instructions that when executed by a processor configure the    processor to implement an information processing method, the method    comprising: receiving input data from a shock sensor which outputs    data based on a shock on the shock sensor; receiving time-series    data from a motion sensor that senses motion of an object; and    identifying with processing circuitry a target segment of the    time-series data, wherein the target segment includes a pre-shock    portion that occurs before a shock event and a post-shock portion    that occurs after the shock event, the shock event is recognized    based on the data from the shock sensor.-   (22) An information processing device including: a sensor data    acquisition unit that acquires first time-series data including a    detected value of a first sensor detecting a shock transferred to a    sensor device mounted directly or indirectly on a user who plays    sports and second time-series data including a detected value of a    second sensor detecting a physical behavior of the sensor device    with a resolution higher than a resolution of the first sensor; and    a segment setting unit that sets an analysis target segment in which    analysis is performed to determine a motion pattern of the user in    the second time-series data based on the first time-series data.-   (23) The information processing device of (22), wherein the segment    setting unit sets the analysis target segment using, as a reference,    a point at which the detected value of the first sensor exceeds a    predetermined threshold value.-   (24) The information processing device of (23), wherein the segment    setting unit sets the analysis target segment using, as a reference,    a segment in which a frequency component of the detected value of    the first sensor includes an eigenfrequency of a mounted object of    the sensor device.-   (25) The information processing device of any one of (22) to (24),    further including: an analysis unit that determines the motion    pattern of the user based on the second time-series data in the    analysis target segment.-   (26) The information processing device of any one of (22) to (25),    further including: an output unit that outputs information    indicating the motion pattern of the user.-   (27) The information processing device of any one of (22) to (26),    wherein the output unit displays an image or text indicating the    motion pattern of the user.-   (28) The information processing device of any one of (22) to (27),    wherein the analysis unit determines the motion pattern of the user    also based on the first time-series data.-   (29) The information processing device of any one of (22) to (28),    wherein the second sensor includes an acceleration sensor, a gyro    sensor, or a geomagnetic sensor.-   (30) The information processing device of any one of (22) to (29),    wherein the first sensor includes a uniaxial acceleration sensor and    the second sensor includes a triaxial acceleration sensor.-   (31) A sensor device which is mounted directly or indirectly on a    user who plays sports, the sensor device including: a first sensor    that detects a shock transferred to the sensor device; a second    sensor that detects a physical behavior of the sensor device with a    resolution higher than a resolution of the first sensor; and a    sensor data preprocessing unit that outputs analysis target data    provided for analysis performed to determine a motion pattern of the    user based on first time-series data including a detected value of    the first sensor and second time-series data including a detected    value of the second sensor.-   (32) The sensor device according to (31), wherein the sensor data    preprocessing unit sets an analysis target segment in which the    analysis is performed in the second time-series data based on the    first time-series data and outputs the second time-series data in    the analysis target segment as the analysis target data.-   (33) The sensor device according to (31) or (32), further including:    a communication unit that transmits the analysis target data to an    analysis device.-   (34) An information processing system including: a sensor device    that is mounted directly or indirectly on a user who plays sports;    and an analysis device that determines a motion pattern of the user    by analyzing analysis target data transmitted from the sensor    device, wherein the sensor device includes a first sensor that    detects a shock transferred to the sensor device, a second sensor    that detects a physical behavior of the sensor device with a    resolution higher than a resolution of the first sensor, and a    sensor data preprocessing unit that generates the analysis target    data based on first time-series data including a detected value of    the first sensor and second time-series data including a detected    value of the second sensor, and a communication unit that transmits    the analysis target data to the analysis device, wherein the    analysis device includes a communication unit that receives the    analysis target data, and an analysis unit that determines the    motion pattern of the user based on the analysis target data,    wherein one of the sensor device and the analysis device includes a    segment setting unit that sets an analysis target segment in the    second time-series data based on the first time-series data included    in the analysis target data, and wherein the analysis unit    determines the motion pattern of the user based on the second    time-series data in the analysis target segment.-   (35) A non-transitory computer-readable storage medium having a    program stored therein, the program causing a computer to execute: a    function of acquiring first time-series data including a detected    value of a first sensor detecting a shock transferred to a sensor    device mounted directly or indirectly on a user who plays sports and    second time-series data including a detected value of a second    sensor detecting a physical behavior of the sensor device with a    resolution higher than a resolution of the first sensor; and a    function of setting an analysis target segment in which analysis is    performed to determine a motion pattern of the user in the second    time-series data based on the first time-series data.-   (36) A non-transitory computer-readable storage medium having a    program stored therein, the program causing a computer included in a    sensor device mounted directly or indirectly on a user who plays    sports to execute: a function of outputting analysis target data    provided for analysis performed to determine a motion pattern of the    user based on first time-series data including a detected value of a    first sensor detecting a shock transferred to the sensor device and    second time-series data including a detected value of a second    sensor detecting a physical behavior of the sensor device with a    resolution higher than a resolution of the first sensor.

What is claimed is:
 1. An information processing system comprising: ashock sensor; processing circuitry configured to, for each of pluralshock events, receive input data from the shock sensor which outputsdata defining a respective shock event, receive time-series data outputfrom a motion sensor that senses motion of an object, the shock sensorhaving a greater dynamic range in acceleration than the motion sensor,set a length of a pre-shock portion of a target segment of thetime-series data as a length of the longest pre-shock portion amongplural predetermined motion patterns, set a length of a post-shockportion of the target segment as a length of the longest post-shockportion among the plural predetermined motion patterns, identify thetarget segment according to the defined respective shock event and theset lengths of the pre-shock portion and the post-shock portion,identify an impact position on the object, and identify the motion ofthe object as one of the plural predetermined motion patterns based onthe identified target segment of the time-series data and a storeddictionary defining each of the plural predetermined motion patterns;and a display configured to notify a user of the identified one of theplural predetermined motion patterns and display an impact positionindicator on the object which is in an orientation corresponding to anactual orientation of the object at a time of impact based on theidentified one of the plural predetermined motion patterns and theidentified impact position.
 2. The information processing system ofclaim 1, wherein each of the plural shock events includes an impactevent including an impact on the object caused by a movement of a user.3. The information processing system of claim 2, wherein the object isan object held by the user, and the impact event includes a collision bythe object and another object.
 4. The information processing system ofclaim 3, wherein the another object is one of a tennis ball, a golfball, and a baseball.
 5. The information processing system of claim 1,wherein the object is a part of a body of a user.
 6. The informationprocessing system of claim 1, wherein the shock sensor and the motionsensor are acceleration sensors.
 7. The information processing system ofclaim 1, wherein the system includes the motion sensor, the motionsensor having a greater resolution of acceleration than the shocksensor.
 8. The information processing system of claim 7, wherein themotion sensor being configured to detect triaxial acceleration andangular speed.
 9. The information processing system of claim 7, whereinthe motion sensor transmits the time-series data to the processingcircuitry wirelessly.
 10. The information processing system of claim 1,wherein the display is configured to display an indication of an impactposition of another object on the object.
 11. The information processingsystem of claim 1, wherein the display is configured to display at leastone index value associated with an actual motion pattern of the object.12. The information processing system of claim 1, wherein the processingcircuitry is configured to identify the target segment by determiningwhen the input data from the shock sensor exceeds a predeterminedthreshold, and a frequency of a signal described by the input data fromthe shock sensor.
 13. The information processing system of claim 1,wherein the information processing system is embodied in a watch-typedevice, and the processing circuitry is configured by a downloadablesoftware application to identify the target segment.
 14. An informationprocessing method comprising: for each of plural shock events, receivinginput data from a shock sensor which is configured to output datadefining a respective shock event; receiving time-series data from amotion sensor that senses motion of an object, the shock sensor having agreater dynamic range in acceleration than the motion sensor; setting alength of a pre-shock portion of a target segment of the time-seriesdata as a length of the longest pre-shock portion among pluralpredetermined motion patterns; setting a length of a post-shock portionof the target segment as a length of the longest post-shock portionamong the plural predetermined motion patterns; identifying withprocessing circuitry the target segment according to the definedrespective shock event and the set lengths of the pre-shock portion andthe post-shock portion; identifying an impact position on the object;identifying the motion of the object as one of the plural predeterminedmotion patterns based on the identified target segment of thetime-series data and a stored dictionary defining each of the pluralpredetermined motion patterns; and notifying a user, via a display, ofthe identified one of the plural predetermined motion patterns anddisplaying an impact position indicator on the object which is in anorientation corresponding to an actual orientation of the object at atime of impact based on the identified one of the plural predeterminedmotion patterns and the identified impact position.
 15. A non-transitorycomputer readable storage device including instructions that whenexecuted by a processor configure the processor to implement aninformation processing method, the method comprising: receiving inputdata from a shock sensor which is configured to output data defining arespective shock event; receiving time-series data from a motion sensorthat senses motion of an object, the shock sensor having a greaterdynamic range in acceleration than the motion sensor; setting a lengthof a pre-shock portion of a target segment of the time-series data as alength of the longest pre-shock portion among plural predeterminedmotion patterns; setting a length of a post-shock portion of the targetsegment as a length of the longest post-shock portion among the pluralpredetermined motion patterns; identifying with processing circuitry thetarget segment according to the defined respective shock event and theset lengths of the pre-shock portion and the post-shock portion;identifying an impact position on the object; identifying the motion ofthe object as one of the plural predetermined motion patterns based onthe identified target segment of the time-series data and a storeddictionary defining each of the plural predetermined motion patterns;and notifying a user, via a display, of the identified one of the pluralpredetermined motion patterns and displaying an impact positionindicator on the object which is in an orientation corresponding to anactual orientation of the object at a time of impact based on theidentified one of the plural predetermined motion patterns and theidentified impact position.